A Metric Learning Reality Check
Research output: Contribution to journal › Conference article › Research › peer-review
Deep metric learning papers from the past four years have consistently claimed great advances in accuracy, often more than doubling the performance of decade-old methods. In this paper, we take a closer look at the field to see if this is actually true. We find flaws in the experimental methodology of numerous metric learning papers, and show that the actual improvements over time have been marginal at best. Code is available at github.com/KevinMusgrave/powerful-benchmarker.
Original language | English |
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Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Pages (from-to) | 681-699 |
Number of pages | 19 |
ISSN | 0302-9743 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Event | 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: 23 Aug 2020 → 28 Aug 2020 |
Conference
Conference | 16th European Conference on Computer Vision, ECCV 2020 |
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Country | United Kingdom |
City | Glasgow |
Period | 23/08/2020 → 28/08/2020 |
Bibliographical note
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
- Deep metric learning
Research areas
ID: 301819335